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AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data | |
2022 | |
发表期刊 | IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING |
ISSN | 2573-0436 |
EISSN | 2333-9403 |
卷号 | 8 |
发表状态 | 已发表 |
DOI | 10.1109/TCI.2022.3155379 |
摘要 | Photoacoustic (PA) imaging is a biomedical imaging modality capable of acquiring high-contrast images of optical absorption at depths much greater than traditional optical imaging techniques. However, practical instrumentation and geometry limit the number of available acoustic sensors surrounding the imaging target, which results in the sparsity of sensor data. Conventional PA image reconstruction methods give severe artifacts when they are applied directly to the sparse PA data. In this paper, we firstly propose to employ a novel signal processing method to make sparse PA raw data more suitable for the neural network, concurrently speeding up image reconstruction. Then we propose Attention Steered Network (AS-Net) for PA reconstruction with multi-feature fusion. AS-Net is validated on different datasets, including simulated photoacoustic data from fundus vasculature phantoms and experimental data from in vivo fish and mice. Notably, the method is also able to eliminate some artifacts present in the ground truth for in vivo data. Results demonstrated that our method provides superior reconstructions at a faster speed. IEEE |
关键词 | Compressed sensing Deep learning Medical imaging Photoacoustic effect Semantics Attention Business process reengineering Deep learning Features extraction Images reconstruction Multi-feature fusion Photoacoustic tomography Reconstruction Sparse matrices Sparse sampling |
URL | 查看原文 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61805139] ; United Imaging Intelligence[2019X0203-501-02] |
WOS研究方向 | Engineering ; Imaging Science & Photographic Technology |
WOS类目 | Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000769970400001 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20221011757788 |
EI主题词 | Image reconstruction |
EI分类号 | 461.1 Biomedical Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 716.1 Information Theory and Signal Processing ; 741.1 Light/Optics ; 746 Imaging Techniques ; 751.1 Acoustic Waves |
原始文献类型 | Article in Press |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/161470 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_高飞组 信息科学与技术学院_博士生 |
通讯作者 | Liu, Jiang; Gao, Fei |
作者单位 | 1.ShanghaiTech Univ, Sch Informat Sci & Technol, Hybrid Imaging Syst Lab, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China 5.Chinese Acad Sci, Cixi Inst Biomed Engn, Shanghai 200050, Peoples R China 6.Shanghai Engn Res Ctr Energy Efficient & Custom A, Shanghai 201210, Peoples R China |
第一作者单位 | 信息科学与技术学院 |
通讯作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Guo, Mengjie,Lan, Hengrong,Yang, Changchun,et al. AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data[J]. IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING,2022,8. |
APA | Guo, Mengjie,Lan, Hengrong,Yang, Changchun,Liu, Jiang,&Gao, Fei.(2022).AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data.IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING,8. |
MLA | Guo, Mengjie,et al."AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data".IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 8(2022). |
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